Iterative reconstruction methods, such as e.g. AW-OSEM (Attenuation Weighted Ordered Subsets Expectation Maximization) or OP-OSEM (Ordinary Poisson Ordered Subsets Expectation Maximization), which are described in detail for example in the publication by Hudson, H. M., Larkin, R. S. (1994) “Accelerated image reconstruction using ordered subsets of projection data”, IEEE Trans. Medical Imaging, 13 (4), 601-609, are used in the reconstruction of positron emission tomography images in conventional positron emission tomography systems (PET systems). These reconstruction algorithms compute images which, using a given Poisson noise model and a measured attenuation map (μ-map) exhibit a close match to the measured data.
On account of their iterative nature these algorithms are very time-intensive and their computation time rises in a linear manner with the number of iterations. In order to reduce the computation time at least partly, just an ordered number of subgroups of raw data groups, known as bins, can be used for each updating of the reconstructed image during the iterations. But even then the computation times are still so long that the reconstruction is aborted in typical clinical applications before the algorithms converge.
In combined magnetic resonance positron emission tomography systems (MR-PET) a human attenuation correction for the attenuation map for positron emission tomography based on magnetic resonance data is also determined. Therefore in MR-PET systems the reconstruction times are slowed down further by the fact that the attenuation correction is based on images and is usually performed on the basis of a combined image volume from a number of table stations, wherein a coverage of the Field of View (FoV) of the magnetic resonance system which is as large as possible is preferred in order to be able to use a largest possible anatomical context for model-based or model-supported segmentation algorithms of the lungs or the bones for example. An overview of magnetic resonance (MR)-based attenuation correction techniques can be found in the publication by Hofmann, M., B. Pichler, B. Schölkopf and T. Beyer: “Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques” European Journal of Nuclear Medicine and Molecular Imaging 36(Supplement 1), 93-104 (03 2009). However, the consequence of using the largest possible FoV coverage is that a reconstruction of an MR-PET at different table stations, known as an MR-PET Multi-Station-Scan, can only be performed after the acquisition of the last table station, when a combination with a greatest possible coverage is fully available, while for example in a positron emission tomography/computed tomography (PET/CT) a computation for each table position can be performed immediately after the acquisition of the emission data has been concluded.
As previously described, magnetic-resonance-based attenuation correction is image-based and can furthermore exhibit different levels of complexity and precision in different stages. Each of these stages can depend on previous results and thus a sequential computation can be necessary. For example in a first step of the computation of the MR-based attenuation correction a simple foreground/background separation can be carried out, in a second step a lung segmentation can be performed within the foreground area, and a third step can consist of a segmentation of fatty and soft tissue within the foreground area. A next step can for example comprise a segmentation of a bone mask with the aid of orientation points and model-based image-processing technologies. In a further step objects outside the regular field of view can be added in, as is described for example in the publication by Johan Nuyts, Christian Michel, Matthias Fenchel, Girish Bal, Charles Watson. “Completion of a truncated attenuation image from the attenuated emission data”. IEEE Nuclear Science Symposium Conference Record, Knoxville, 2010. A further step can comprise the computation or a so-called hardware attenuation correction map for each table station. This hardware attenuation correction map takes account of devices, such as e.g. local coils, which are disposed in the MR-PET system in the examination area. In a further step the computed segmented attenuation map can be refined locally in order for example to improve a consistency or freedom from contradictions, for example on the basis of a DCC (Discrete Consistency Condition) method or an MLAA (Maximum Likelihood of Attenuation and Activity) method, for example by the previously defined linear attenuation coefficients being adapted to more individual specific linear attenuation coefficients.
The steps given above need a significant time for the computation and can therefore delay the computation of the final PET image by a few minutes. The attenuation-corrected PET images are needed for a final quality check of the images before the patient can be released from the magnet resonance positron emission tomography system. The long computation times mean that a longer time is spent by the patient in the MR-PET system, which can be unpleasant for patients and additionally blocks the system for further patients.
Because of the fact that the attenuation correction in an MR-PET is image based and is based on a combined image, which preferably uses a largest possible field of view coverage, the attenuation-corrected PET reconstruction is only determined when the finished and complete attenuation correction map is available. In other words the attenuation-corrected PET reconstruction is delayed until such time as the finished attenuation correction map is available. FIG. 1 shows a schematic of a sequence of an attenuation-corrected PET reconstruction. First of all magnetic resonance and positron emission tomography data is acquired consecutively over time at three different table positions (steps 110-112) in order to acquire a desired coverage area of the patient. Seen in terms of time the attenuation correction maps are computed after the last acquisition 112 in different steps 113-115, which can at least partly depend on one another. Then, in step 116, the positron emission tomography images are reconstructed for example using an OSEM (Ordered Subsets Expectation Maximization) technology, which in its turn can require different iterations. Only after conclusion of the PET reconstruction can quality checks and evaluations of the positron emission tomography images take place and the patient be released from the MR-PET system.
In practice the computation of steps 113-116 can require five minutes for example for a usual examination with five table positions. These computation times can increase with more table positions and more attenuation correction algorithms.
The previously described time-intensive sequences during determination of the attenuation correction maps and the positron-emission tomography images cannot just occur in conjunction with a magnetic resonance positron emission tomography, but can also occur in other hybrid systems comprising a positron emission tomography system and a further tomography system. The further tomography system can be any given slice imaging modality, such as an ultrasound tomography system for example.